Self-Supervised Convolutional Neural Network Learning in a Hybrid Approach Framework to Estimate Chlorophyll and Nitrogen Content of Maize from Hyperspectral Images

The new generation of available (i.e., PRISMA, ENMAP, DESIS) and future (i.e., ESA-CHIME, NASA-SBG) spaceborne hyperspectral missions provide unprecedented data for environmental and agricultural monitoring, such as crop trait assessment. This paper focuses on retrieving two crop traits, specificall...

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Veröffentlicht in:Remote sensing (Basel, Switzerland) Jg. 15; H. 19; S. 4765
Hauptverfasser: Gallo, Ignazio, Boschetti, Mirco, Rehman, Anwar Ur, Candiani, Gabriele
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.10.2023
Schlagworte:
Agricultural production
Algorithms
Artificial intelligence
Artificial neural networks
calibration
canopy
Cereal crops
Chlorophyll
Comparative analysis
Composition
convolutional neural network
Corn
Crops
Datasets
deep learning
Environmental aspects
hybrid approach
hyper-spectral images
Hyperspectral imaging
Image acquisition
Learning algorithms
Machine learning
Measurement
Multispectral photography
Neural networks
Nitrogen
nitrogen content
Nitrogen in the body
Physiological aspects
prediction
Radiative transfer
Remote sensing
Satellites
Self-supervised learning
Sensors
Simulation
Testing
Unmanned aerial vehicles
Italy
ISSN:2072-4292, 2072-4292
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Zusammenfassung:The new generation of available (i.e., PRISMA, ENMAP, DESIS) and future (i.e., ESA-CHIME, NASA-SBG) spaceborne hyperspectral missions provide unprecedented data for environmental and agricultural monitoring, such as crop trait assessment. This paper focuses on retrieving two crop traits, specifically Chlorophyll and Nitrogen content at the canopy level (CCC and CNC), starting from hyperspectral images acquired during the CHIME-RCS project, exploiting a self-supervised learning (SSL) technique. SSL is a machine learning paradigm that leverages unlabeled data to generate valuable representations for downstream tasks, bridging the gap between unsupervised and supervised learning. The proposed method comprises pre-training and fine-tuning procedures: in the first stage, a de-noising Convolutional Autoencoder is trained using pairs of noisy and clean CHIME-like images; the pre-trained Encoder network is utilized as-is or fine-tuned in the second stage. The paper demonstrates the applicability of this technique in hybrid approach methods that combine Radiative Transfer Modelling (RTM) and Machine Learning Regression Algorithm (MLRA) to set up a retrieval schema able to estimate crop traits from new generation space-born hyperspectral data. The results showcase excellent prediction accuracy for estimating CCC (R2 = 0.8318; RMSE = 0.2490) and CNC (R2 = 0.9186; RMSE = 0.7908) for maize crops from CHIME-like images without requiring further ground data calibration.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs15194765